Vibration control of a vehicle’s seat suspension featuring a magnetorheological damper based on a new adaptive fuzzy sliding-mode controller

Abstract

This work presents a new design for adaptive fuzzy sliding-mode control based on two methodologies, namely H∞ control and sliding-mode control, and its control effectiveness. This is achieved by implementing a control scheme for vibration control of a vehicle with a seat suspension on which a magnetorheological damper is installed. The sliding surface of sliding-mode control is analysed by separation into two matrices: a Hurwitz-constants matrix and a constant matrix. These matrices are the basis for establishing the proposed control scheme combined with the H∞ technique. The control scheme consisting of the combination of H∞ control and sliding-mode control is reinforced by a new robustness function featuring an exponential function. In this work, a fuzzy logic model, which is well known to be an excellent model for uncertain dynamic systems, is integrated with the proposed control algorithm. The fuzzy logic model adopted in this work is an interval type-2 fuzzy model featuring fast computation of the output. The effectiveness of the proposed control scheme is evaluated through both computer simulations and experimental realization on a vehicle with a seat suspension which is equipped with a magnetorheological damper. In addition, in this work, two existing adaptive controllers are modified and implemented for comparative work with the proposed control scheme. It is shown that the proposed control scheme exhibits a much better vibration control performance than the two existing adaptive controllers do.

A Novel Adaptive PID Controller with Application to Vibration Control of a Semi-Active Vehicle Seat Suspension

“…against any disturbance and the uncertainty of the system. It is noted here that according to the standard ISO 2631-1, the acceleration at the driver position should be less than 2.5 m/s 2 [17]. From the results presented in Figures 7 and 8, the accelerations of the proposed controller shown in Figures 7(d1,d2) and 8(d1,d2), are always less than the standard value of ISO 2631-1.…”

“…The proposed controller proves that the combination of two or more control logics can bring better control responses since the inherent advantages of each controller are retained to guarantee robust stability against any disturbance and the uncertainty of the system. It is noted here that according to the standard ISO 2631-1, the acceleration at the driver position should be less than 2.5 m/s 2 [17]. From the results presented in Figures 7 and 8, the accelerations of the proposed controller shown in Figure 7(d1,d2) and Figure 8(d1,d2), are always less than the standard value of ISO 2631-1.…”

“…It is remarked that the equipment used for this experiment is similarly to the devices used in [17]. The signal from the sensors such as linear variable differential transformer (LVDT) transducer, accelerometers at the seat and driver are used as feedback signals to the controller.…”

“…, m) are fuzzy sets, m is the number of rules, and a j i are interval sets. The calculation process of OIT2FNN is clearly depicted in [17,18]. The defuzzified output is then determined by…”

“…In this work, a model of vehicle MR seat suspension system used in [17] is adopted to demonstrate control effectiveness of the proposed hybrid adaptive controller. The practical photograph and corresponding mechanical model are shown in Figure 2a,b, respectively.…”